Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on federated learning (FL) and secure multi-party computation (MPC). PrivatEyes enables training gaze estimators on multiple local datasets across different users and server-based secure aggregation of the individual estimators' updates. PrivatEyes guarantees that individual gaze data remains private even if a majority of the aggregating servers is malicious. We also introduce a new data leakage attack DualView that shows that PrivatEyes limits the leakage of private training data more effectively than previous approaches. Evaluations on the MPIIGaze, MPIIFaceGaze, GazeCapture, and NVGaze datasets further show that the improved privacy does not lead to a lower gaze estimation accuracy or substantially higher computational costs - both of which are on par with its non-secure counterparts.

翻译：最新的注视估计方法需要大规模训练数据，但其收集与交换过程存在显著隐私风险。本文提出PrivatEyes——首个基于联邦学习（FL）与安全多方计算（MPC）的外观注视估计隐私增强训练方法。PrivatEyes能够在不同用户的多个本地数据集上训练注视估计器，并通过服务器实现各估计器更新的安全聚合。即便多数聚合服务器存在恶意行为，PrivatEyes也能保证个体注视数据的隐私性。我们还提出一种新的数据泄露攻击方法DualView，证明PrivatEyes能比现有方法更有效地限制私有训练数据的泄露。在MPIIGaze、MPIIFaceGaze、GazeCapture和NVGaze数据集上的评估进一步表明，隐私性的提升并未导致注视估计精度下降或计算成本显著增加——二者均与其非安全版本相当。